Thoughts on AI automation, product building, and the systems that help founders move faster.
Buy when the workflow is generic. Build when it touches the data that makes you different. The expensive mistake is not choosing wrong — it's never noticing the switch happened.
Your employees are already using AI you didn't buy — 78% bring their own tools, 57% hide it, and shadow AI now factors into 43% of security incidents. Banning it fails. Here's how to surface it, price it, and convert it into sanctioned capability.
Most companies can see their AI bill. Almost none can predict it. Here's the control system that fixes both — before the finance team forces a freeze.
Agentic AI is software that pursues a goal across multiple steps, choosing its own actions and tools — not a chatbot with a new label. Most of what's sold as agentic isn't. Here's the working definition and the four tests that separate the real thing.
The build quote is the smallest number in the project. Here's the honest total cost of building AI agents in-house — team, maintenance, tokens, and the reliability tax — plus the three cases where building is still right.
Finance has the highest AI adoption of any back-office function and some of the thinnest returns — because most teams automate the judgment and leave the paperwork alone.
AI can resolve half your support tickets without damaging CSAT — but only if you design the escalation path before the automation. Here's the playbook, the honest benchmarks, and the failure modes that made Klarna rehire humans.
Most failed AI automations were doomed at the mapping stage, not the tooling stage. Here's the five-step map that decides whether your project ships.
Hiring in-house costs more and takes longer than most mid-market companies assume. An agency buys speed but can leave nothing behind. The real answer is a sequence, not a side.
Banning ChatGPT doesn't stop data leaks — it just moves them to personal accounts you can't see. Here's the rollout sequence that keeps company data inside the boundary: sanctioned tools, tiered data rules, logging, and one named owner.
Most AI governance advice is written for the Fortune 500. Here are the five controls that actually reduce risk at 50-500 people - inventory, one data rule, an approved-tools list, a named human per decision, and a tested kill switch - plus what to skip.
One workflow ships in weeks. Company-wide AI takes years. Most timelines fail because leaders confuse the two. Here's the realistic 2026 timeline for each layer — and the four things that actually eat the schedule.
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The numbers that actually matter if you're accountable for whether AI works inside your company — and what each one should change about your plan.
RPA executes rules. AI automation handles judgment. Most mid-market teams need both — and the expensive mistake is buying one to do the other's job. Here's the decision line, with the data.
Your team isn't resisting AI — over 90% of them already use it on their own accounts. They just aren't using yours. Adoption is a management problem, not a licensing one.
Not IT, not a committee, and not a Chief AI Officer you can't afford. Here's the ownership model that actually ships automation in a 200-person company.
Most mid-market AI budgets are wrong by 2–3×, because they price the software and ignore the integration, the change management, and the year-two run cost.
Most AI pilots don't fail because the model is bad. They fail because nobody redesigned the work around it, and nobody owned the outcome.
Most AI budgets go to sales and marketing. Most of the returns are hiding in the back office. Here's where the money actually is.
Start with the high-volume, rule-bound back-office work nobody wants to own — and leave anything where a wrong answer is expensive and hard to reverse to a human.
Most companies build what they should have bought, and buy what nobody ends up using. Here are the three questions that tell you which workflow needs ChatGPT, which needs a point tool, and which actually justifies a custom agent.
A practical roadmap for mid-market operators: where to start, what it costs, how long it takes, and the failure modes that kill most AI projects before they reach the P&L.
